Everyone knows that Swift is an open-source language from Apple and is available on macOS machines. Some of you might even know that Swift can be compiled on iPadOS.
But did you know that Swift is also available on Ubuntu? If yes, have you ever tried it?
Let’s try this out here—this post will walk you through compiling a Swift file on Ubuntu.
In this tutorial, we’re going to talk about a type of unsupervised learning model known as Boltzmann machines. We assume the reader is well-versed in machine learning and deep learning. We’ll use PyTorch to build a simple model using restricted Boltzmann machines. This model will predict whether or not a user will like a movie.
A Boltzmann machine defines a probability distribution over binary-valued patterns. What makes Boltzmann machine models different from other deep learning models is that they’re undirected and don’t have an output layer. The other key difference is that all the hidden and visible nodes are all connected with each other. Due to this interconnection, Boltzmann machines can generate data on their own. As such, it can be classified as a generative deep learning model.
Continue reading Introduction to Restricted Boltzmann Machines Using PyTorch
But there was one major issue with Prisma. It had to be run through JVM and had memory issues. The Prisma Framework is rewritten in Rust, and it addresses the issues from Prisma 1.
Prisma 1 also required a server in front of your backend server, while with the Prisma Framework, the query engine is now a bundled executable that’s run alongside the backend on the same server.
Continue reading Getting Started with the Prisma Framework (formerly Prisma 2) in React Native
In my previous article, we had a detailed discussion on the one-shot learning problem and how various algorithms tackled it in order to determine which approach was the most effective, and we settled on siamese networks. I’d encourage you to check it out:
But is this enough?Not quite! We need some hands-on-experience to actually learn how implement it. And what could be more relatable these days than facial recognition, which just happens to be a perfect use case for one-shot learning?
Continue reading One-Shot Learning (Part 2/2): Facial Recognition Using a Siamese Network
Big data and machine learning, while two separate concepts, remain interwoven in many aspects. The ability to process vast piles of data for machine learning tasks is a requirement of the field.
Apache Spark is a great framework when it comes to large-scale data processing (and has been for a while), enabling you to work with a range of big data problems. Apart from supporting cluster computing and distributivity with various languages such Java, Scala, and Python, Spark offers support for a variety of ML capabilities via its native libraries. However, its selling point remains its potential for ETL processing with large scale datasets.
Continue reading H2O AutoML + Big Data Processing with Apache Spark
This post is a part of a series about feature engineering techniques for machine learning with python.
You can check out the rest of the articles below — links will be added as posts go live:
Welcome back! Part two in our series intends to give you an overview of the type variable, which you can find in a given dataset. It’s essential to know the different variables’ types to better to engineer them.
Continue reading Hands-on with Feature Engineering Techniques: Variables Types
This article is a part of a series about feature selection techniques. You can check out the rest of the articles as they are/become available:
Welcome back! This post will give you an overview of wrapper methods for feature selection.
In the last post in the series, we explored the filter methods that tend to select features independently and work with (essentially) any machine learning algorithm. Consequently, one of the disadvantages of these methods is that they tend to ignore the effect of the selected feature subset on the performance of the algorithm.
Continue reading Hands-on with Feature Selection Techniques: Wrapper Methods
Waymo has driven more than 20 million miles on public roads in over 25 cities. They also drove tens of billions of miles in simulations (as we’ll see later in the article). Additionally, Waymo is operating a taxi service in the United States, transporting passengers—for real—without a driver.
Given their growing presence in the real world, I want to dive deep into Waymo’s technology so you can understand what’s actually behind this giant.
In this tutorial, we’ll continue learning the various use-cases of the TensorFlow.js library. Our previous tutorial used this library for real-time human pose estimation. Here, we’re going to detect hand gestures using the library.
In this tutorial, we’re going to focus our pose algorithm on a smaller area—human hands. We are going to detect the hand poses and gestures using the TensorFlow.js library. Like in the previous tutorial, we are going to make use of a webcam for gesture detection and canvas for drawing or displaying the result of the detection.
Continue reading How to detect a “thumbs-up” in the browser with TensorFlow.js
Machine learning is a buzzword nowadays. There are plenty of theories going around, but it’s hard to see real applications that can be built by an indie developer.
Developing an end-to-end machine learning system requires a wide range of expertise in areas like linear algebra, vector calculus, statistics, and optimization.
Therefore, from a developer’s perspective, there’s a high learning curve that stands in the way, but the latest tools take care of most of the work for developers, leaving them free to code.
Continue reading “How to retrain ResNet in Keras and use it in an iOS App via Core ML”
